The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventor, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.
Bipolar disorder (BP) is a common and severe psychiatric illness characterized by pathological swings of mania and depression and is associated with devastating personal, social, and vocational consequences (suicide occurs in up to 20% of cases, by some reports). Bipolar disorder is among the leading causes of disability worldwide. The cost in the United States alone was estimated at $45 billion annually (in 1991 dollars). These economic and human costs, along with the rapidly increasing price of health care provide the impetus for a major paradigm shift in health care service delivery, namely to monitor and prioritize care with a focus on prevention.
Speech patterns have been effectively used in clinical assessment for both medical and psychiatric disorders. Clinicians are trained to record their observations of speech and language, which become a critical component of the diagnostic process. Recently, there have been research efforts exploring computational speech analysis as a way to assess and monitor the mental state of individuals suffering from a variety of psychological illnesses, specifically major depression (MD), autism, and post-traumatic stress disorder (PTSD).
For example, stress and anxiety have been studied extensively and elements of speech have been correlated with subjectively reported stress in PTSD. Other research efforts have demonstrated the efficacy of speech-based assessments for autism focusing on diagnosis, in addition to predicting the course and severity of the illness. Variations in speech patterns have also been used for computational detection and severity assessment in major depressive disorder.
However, most work in these areas focuses on the assessment of participants over short periods of time, at most several weeks, rendering it challenging to measure the natural fluctuations that accompany illness trajectories. Additionally, the speech input is often highly structured and collected in controlled environments. This limitation in speech collection precludes an understanding of how acoustic patterns characteristic of natural speech variation correlate with mood symptomology.